System and method for estimating a quantity of a blood component in a fluid canister

ABSTRACT

A variation of a method for estimating a quantity of a blood component in a fluid canister includes: within an image of a canister, identifying a reference marker on the canister; selecting an area of the image based on the reference marking; correlating a portion of the selected area with a fluid level within the canister; estimating a volume of fluid within the canister based on the fluid level; extracting a feature from the selected area; correlating the extracted featured with a concentration of a blood component within the canister; and estimating a quantity of the blood component within the canister based on the estimated volume and the concentration of the blood component within the canister.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/876,628, filed 6, Oct. 2015, (now U.S. Pat. No. 9,595,104), which isa continuation of U.S. patent application Ser. No. 14/613,807 (now U.S.Pat. No. 9,171,368), filed 4, Feb. 2015, which is a continuation of U.S.patent application Ser. No. 13/738,919 (now U.S. Pat. No. 8,983,167),filed 10, Jan. 2013, which claims the benefit of U.S. Provisional PatentApplication No. 61/703,179, filed on 19, Sep. 2012, U.S. ProvisionalPatent Application No. 61/646,822, filed 14, May 2012, and U.S.Provisional Patent Application No. 61/722,780, filed on 5, Nov. 2012,all of which are incorporated in their entireties by this reference.

This application is related to U.S. patent application Ser. No.13/544,646, filed on 9, Jul. 2012, which is incorporated in its entiretyby this reference.

TECHNICAL FIELD

This invention relates generally to the surgical field, and morespecifically to a new and useful system and method for estimating theextracorporeal blood volume in a canister for use in surgical practice.

BACKGROUND

Overestimation and underestimation of patient blood loss is asignificant contributor to high operating and surgical costs forhospitals, clinics and other medical facilities. Specifically,overestimation of patient blood loss results in wasted transfusion-gradeblood and higher operating costs for medical institutions and can leadto blood shortages. Underestimation of patient blood loss is a keycontributor of delayed resuscitation and transfusion in the event ofhemorrhage and has been associated with billions of dollars in avoidablepatient infections, re-hospitalizations, ad lawsuits annually.

Thus, there is a need in the surgical field for a new and useful methodfor estimating a quantity of a blood component in a fluid canister. Thisinvention provides such a new and useful system and method.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flowchart representation of a method of one embodiment;

FIG. 2 is a flowchart representation of one variation of the method;

FIG. 3 is a flowchart representation of one variation of the method;

FIGS. 4A and 4B are graphical representations in accordance with onevariation of the method;

FIGS. 5A, 5B, 5C, and 5D are graphical representations in accordancewith variations of the method;

FIGS. 6A and 6B are graphical representations in accordance with onevariation of the method;

FIGS. 7A and 7B are graphical representations in accordance with onevariation of the method;

FIG. 8 is a graphical representation in accordance with one variation ofthe method; and

FIG. 9 is a schematic representation of a system of one embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of preferred embodiments of the invention isnot intended to limit the invention to these preferred embodiments, butrather to enable any person skilled in the art to make and use thisinvention.

1. Methods

As shown in FIGS. 1 and 2, a method S100 for estimating a quantity of ablood component in a fluid canister includes: within an image of acanister, identifying a reference marker on the canister in Block S110;selecting an area of the image based on the reference marker in BlockS120; correlating a portion of the selected area with a fluid levelwithin the canister in Block S130; estimating a volume of fluid withinthe canister based on the fluid level in Block S140; extracting afeature from the selected area in Block S150; correlating the extractedfeatured with a concentration of a blood component within the canisterin Block S160; and estimating a quantity of the blood component withinthe canister based on the estimated volume and the concentration of theblood component within the canister in Block S170.

As shown in FIG. 3, one variation of the method S100 includes: removinga background from an image of a canister in Block S112; correlating asegment of the image with a portion of the canister containing fluid inBlock S120; estimating a volume of fluid within the canister based onthe segment in Block S140; extracting a color feature from a pixelwithin the segment in Block S150; correlating the color feature with aconcentration of a blood component within the canister in Block S160;and estimating a content of the blood component within the canisterbased on the estimated volume of fluid and the concentration of theblood component within the fluid canister in Block S170.

The method S100 functions to implement machine vision to estimate thecontent of a blood component within a fluid canister. Generally, themethod S100 can analyze an image of a fluid canister to determine afluid volume within the canister in Block S140 and a concentration ofthe blood component in Block S160, data that can be combined to derivethe content of the blood component within the canister in Block S170.The method S100 can therefore recite a variation of and/or implementtechniques described in U.S. patent application Ser. No. 13/544,646,which is incorporated herein by reference.

The blood component can be any of whole blood, red blood cells,hemoglobin, platelets, plasma, or white blood cells. However, the methodS100 can also implement Block S180, which recites estimating a quantityof a non-blood component within the canister based on the estimatedvolume and the concentration of the non-blood component within thecanister. The non-blood component can be saline, ascites, bile, irrigantsaliva, gastric fluid, mucus, pleural fluid, urine, fecal matter, or anyother bodily fluid of a patient.

The fluid canister can be a suction canister implemented in a surgicalor other medical, clinical, or hospital setting to collect blood andother bodily fluids, wherein the fluid canister can be translucent orsubstantially transparent such that the method S100 can identify andanalyze fluid contained within the canister. The canister canalternatively be a blood salvage canister, an intravenous fluid bag, orany other suitable blood- or fluid-bearing container for collectingsurgical waste or recovering biological fluid. For example, the canistercan be a surgical fluid canister including: a translucent containerconfigured to hold a fluid, the container including a wall and a seriesof horizontal fluid volume indicator markings arranged along the walland visible from external the container; ad an anti-glare strip arrangedon an external surface of the wall. The anti-glare strip can be arrangedon the container such that the area selected from an image of thecanister in Block S120 of the method S100 includes at least a portion ofthe anti-glare strip. The anti-glare strip can therefore be positionedon the container to reduce glare on the portion of the containercorresponding to the selected area of the image, thus reducingglare-induced errors in the estimated content of the blood component inthe canister. The anti-glare strip can be an adhesive strip, such asScotch tape by 3M or a marking printed on an external surface of thesurgical fluid canister, and the anti-glare strip can include a dull,matte, satin, or other suitable anti-glare surface finish. Theanti-glare strip can also be a narrow strip extending from proximal thebottom of the surgical fluid canister to proximal the top of thesurgical fluid canister, though the anti-glare strip can be of any otherform, geometry, material, or surface finish and can be applied to thesurgical fluid canister in any other way. However, the fluid canistercan be any other suitable type of canister including any other suitablefeature.

Because any of the foregoing blood and non-blood fluids can be collectedin the fluid canister in any quantity ad concentration during a surgeryor other medical event, and because fluid content and concentrationcannot be estimated substantially in real time through canister volumereadings alone, the method S100 can be useful in quantifying an amountand/or concentration of a blood component (e.g., hemoglobin) and/orother fluids (e.g., saline). Furthermore, from this derived data, thevolume of extracorporeal blood in the fluid canister can be estimated,thus enabling substantially comprehensive blood loss monitoring,particularly when implemented alongside any of the method S100 sdescribed in U.S. patent application Ser. No. 13/544,646, which isincorporated herein by reference, which describes estimatingextracorporeal blood volume in surgical sponges, in surgical towels,and/or on other surfaces.

The method S100 is can be implemented by a computer system as a fluidcanister analyzer that analyzes a photographic image to estimate thecontent of a fluid canister. The computer system can be cloud-based(e.g., Amazon EC2 or EC3), a mainframe computer system, a grid-computersystem, or any other suitable computer system. The method S100 cantherefore be implemented by a handheld (e.g., mobile) computing device,such by a smartphone, digital music player, or tablet computer executinga native blood component analysis application as shown in FIGS. 1 and4A. For example, a camera integral with the computing device can capturethe image of the fluid canister, and a processor integral with thecomputing device implement Blocks S110, S120, S130, etc. Additionally oralternatively, the computing device can communicate with a remoteserver, such as over the Internet via a wireless connection, wherein theserver performs at least some Blocks of the method S100 and wherein atleast some of the outputs of the method S100 are transmitted back to thecomputing device for further analysis and/or subsequent release to auser. The computing device can also include or be coupled to a digitaldisplay such that the method S100 can display information to a user(e.g., a nurse or anesthesiologist) through the display.

Alternatively, the method S100 can be implemented as a standalone bloodvolume estimation system including a fluid canister, a fluid canisterstand, a camera, a camera stand configured to support the cameraadjacent the fluid canister, a digital display, a processor configuredto perform at least a portion of the method S100, and/or a communicationmodule configured to communicate with a remote server that performs atleast a portion of the method S100. In this implementation, the cameracan be substantially non-transiently positioned relative to a fluidcanister stand such that the camera remains in a suitable position tocapture an image of a canister substantially throughout a surgery orother medical event and/or until the canister is full. This can enablethe camera to regularly capture and analyze images of the fluidcanister, such as every thirty seconds or every two minutes. This systemimplementing the method S100 can further communicate (e.g., viaBluetooth) with another one or more systems implementing any one or moreof methods of U.S. patent application Ser. No. 13/544,646 to enable asubstantially comprehensive estimate of extracorporeal blood volume andthus total patient blood loss. However, the method S100 can beimplemented in or by any other computer system, computing device, orcombination thereof.

As shown in FIG. 3, one variation of the method S100 includes BlockS102, which recites capturing an image of the canister. Block S102 caninterface with a camera or other suitable optical sensor to capture theimage of a field of view of the camera or optical sensor, wherein thecanister is in the field of view of the camera or optical sensor. Asshown in FIG. 5A, Block S102 can capture the image that is a static,single-frame image including at least a portion of the fluid canister.Alternatively, Block S102 can capture the image that is a multi-framevideo feed including multiple static images of the fluid canister. Theimage can be a color image, a black and white image, a grayscale image,an infrared image, a field of view of an optical sensor, a fingerprintof a field of view of an optical sensor, a point cloud, or any othersuitable type of image.

In one implementation, Block S102 captures the image of the canisteraccording to a time schedule, such as every thirty seconds or every twominutes during a surgery. Alternatively, Block S102 can implementmachine vision and/or machine recognition techniques to identify thecanister within the field of view of the optical sensor and triggerimage capture once a canister (or other blood-containing item) isdetected. For example, Block S102 can capture an image of the field ofview of the canister each time a user holds the camera (e.g., thecomputing device that incorporates the camera) up to the fluid canister.Similarly, Block S102 can cooperate with Block S140 to capture the imageof the canister once a threshold increase is canister fluid volume isdetected. Therefore, Block S112 can capture images of the canisterautomatically, such as based on a timer, changes in canister fluidvolume, or availability of the canister for imaging, which can enablethe method S100 to track fluid collection in the canister over time, asshown in FIGS. 7A and 7B. This can be useful in mapping trends inpatient fluid loss and/or predicting future patient fluid (e.g., blood)loss. Alternatively, Block S102 can capture the image of the canisteraccording to a manual input, such as from a nurse or anesthesiologist.

In the foregoing implementations, Block S102 can further guide a user incapturing the image of the fluid canister. For example, as shown inFIGS. 4A and 4B, Block S102 can display an alignment graphic on adisplay of the computing device, wherein the display also functions as aviewfinder for a camera incorporated into the computing device. In thisexample, Block S102 can prompt the user to align an edge of the canisterin the field of view of the camera with the alignment graphic renderedon the display, as shown in FIG. 4A. The alignment graphic can includepoints, lines and/or shapes (e.g., an outline of a canister) to bealigned with a side, feature, decal, and/or the reference marker of oron the fluid canister. Block S102 can thus guide a user in properlypositioning the canister relative the camera (or other optical sensor)in preparation for imaging. The alignment graphics can additionally oralternatively include curves suggestive of a perspective view of thefluid canister, which can guide the user in positioning the canister ina preferred orientation, (e.g., vertical and/or horizontal pitch)relative to and/or distance from the camera. Block S102 can alsointerface with a light source or flash system to control lighting of thecanister during image capture of the canister. Block S102 canadditionally or alternatively alert a user, such as with an audible orvisual alarm, if lighting of the canister was insufficient or too poorto enable substantially accurate estimation of fluid volume of bloodcontent of the canister. Block S102 can thus enable image capture of thefluid canister with a substantially high degree of accuracy andrepeatability with predictable canister positioning, lightingcompensation, etc., which can further enable substantially accurate andrepeatable blood component content estimations in Block S170.

Block S102 can also timestamp each image of the canister as the canisteris filled, replaced, and/or emptied, which can further enable the methodS100 to track changes in fluid level within the canister, map patientblood (and fluid) loss trends, etc. However, Block S102 can function inany other way to capture the image of the canister.

Block S110 of the method S100 recites, within an image of a canister,identifying a reference marker on the canister. Generally, Block S110functions recognize a canister-related marker within the image. Byidentifying the marker, Block S110 can enable analysis of particularportions of the image in subsequent Blocks. Block S110 can implement anysuitable machine vision technique and/or machine learning technique toidentify the reference marker. For example, Block S120 can implementobject localization, segmentation (e.g. edge detection, backgroundsubtraction, grab-cut-based algorithms, etc.), gauging, clustering,pattern recognition, template matching, feature extraction, descriptorextraction (e.g. extraction of texton maps, color histograms, HOG, SIFT,MSER (maximally stable extremal regions for removing blob-features fromthe selected area) etc.), feature dimensionality reduction (e.g. PCA,K-Means, linear discriminant analysis, etc.), feature selection,thresholding, positioning, color analysis, parametric regression,non-parametric regression, unsupervised or semi-supervised parametric ornon-parametric regression, or any other type of machine learning ormachine vision to estimate a physical dimension of the canister. BlockS110 can further compensate for varying lighting conditions of thecanister, variations in fluid compositions canister (e.g., widelyvarying color, transparency, refractive indices, etc.), lens- orsoftware-based optical distortion in the image, or any otherinconsistency or variable prevalent in any use scenario.

In one implementation, Block S110 identifies the reference marker thatis a boundary between the canister and a background, which can enableBlock S110 to remove a portion of the image corresponding to thebackground. In another implementation, Block S110 identifies thereference marker that is a symbol arranged on the canister, as shown inFIG. 5B. For example, the symbol can be a manufacturer's label printedon the canister, a fluid volume scale printed on the canister, a coloreddot (e.g., sticker) adhered to the outside of the canister, a common(e.g., standardized) marking printed on surgical canisters from avariety of manufacturer's, or any other suitable reference marker. In afurther implementation, Block S110 identifies the marker based onsurface finish on the canister. For example, Block S110 can identify themarker that is a portion of the canister than includes a matte or othersubstantially glare-free finish.

Block S110 can additionally or alternatively implement machine visiontechniques to identity the type of fluid canister. For example, BlockS110 can implement template matching to determine the type of thecanister, such as by accessing a template library of reference markers,each reference marker associated with a particular type of canister,such as from a particular manufacturer, of a particular size, and/or ofa particular shape. In this implementation, or more subsequent Blocks ofthe method S100 can be tailored for a specific type of fluid canister,wherein Block S110 functions to set a particular implementation path forthe subsequent Blocks dependent on the particular canister type.However, Block S100 can function in any other way to identify any othersuitable reference marker in the image of the canister and/or the typeof canister.

Block S112 of the method S100 recites removing a background from animage of a canister. Because the background is unlikely to containuseful information related to the volume and/or quality of fluid withinthe fluid canister, Block S112 excludes substantially unnecessaryportions of the image, thus enabling subsequent Blocks of the methodS100 to focus analysis on portions of the image (more likely) containinginformation pertinent to the quality and quantity of fluid in thecanister, as shown in FIGS. 5B and 5C. In one implementation, Block S112applies machine vision, such as edge detection, grab-cut, foregrounddetection, or any other suitable technique, to isolate a portion of theimage associated with the physical canister and to discard the remainderof the image falling outside of a detected boundary of the canister.

In another implementation, Block S112 uses the identified referencemarker to anchor a predefined canister perimeter to the image. BlockS112 can then discard an area of the image that falls outside of thepredefined canister perimeter. For example, Block S112 can select aparticular predefined, canister-shaped boundary according to the sizeand/or geometry of the canister identified in the image in Block S110.Alternatively, Block S112 can receive an input from a user identifyingthe type of fluid canister and subsequently apply a predefined boundaryfilter according to the entered canister type. However, Block S112 canfunction in any other way to remove a background portion from the imageof the fluid canister.

Block S120 of the method S100 recites selecting an area of the imagebased on the reference marker, as shown in FIG. 5D. Generally, BlockS120 functions to select a particular area of the image corresponding toa particular region of interest of the surface of the canister. Theregion of interest can be particularly characteristic of the contents ofthe canister, such as a region of substantially low glare (e.g.,including a matte coating or anti-glare sticker or tape), a regionsubstantially nearest a viewing plane of the camera, a regionsubstantially centered between perceived sides of the canister, and/or aregion of the canister substantially free of additional markings,labels, etc. The selected area can further bisect a surface of the fluidwithin the canister such that Block S130 can subsequently identify thelevel of fluid within the canister based on analysis of the selectionarea. The selected area can therefore be one or more contiguous and/ordiscontiguous pixels within the image and containing informationsubstantially characteristic of the contents of the canister.Furthermore, the selected are can correspond to a surface of thecanister (e.g. a vertical white stripe) that is opaque enough toeliminate background noise but exhibits a substantially abrupt colortransition proximal a fluid surface, thus enabling Block S130 toestimate the fluid height in the canister.

In one example implementation, as shown in FIGS. 5C and 5D, Block S110implements machine vision to identify the reference marker arranged at astandardized position on the canister, and Block S120 select the area ofthe image according to a standardized distance between the referencemarker and the region of interest on the canister, wherein the selectedarea of the image corresponding to the region of interest on thecanister. For example, Block S120 can select the area of the image thatis twenty pixels wide and one hundred pixels tall with geometric centeroffset from the reference marker (e.g., from a determined center pixelof the reference marker) by fifty pixels along the +x axis and seventypixels along the +y axis of the image.

In another example implementation, Block S110 identifies the referencemarker that is a volume marker on the canister, and Block S120 selectsthe area of the image that is a set of pixels adjacent a portion of theimage corresponding to the volume marker. In this exampleimplementation, Block S130 can identify a fluid meniscus within the setof pixels and compare the fluid meniscus to the volume marker in orderto estimate the fluid level in the canister. For example, Block S120 canselect a rectangular area of the image that is twenty pixels wide andone hundred pixels tall with upper right corner of the area offset froma left edge of the volume marker by ten pixels along the −x axis andtwenty pixels along the +y axis of the image.

In yet another example implementation, Block S110 identifies horizontalvolume indicator markings on the fluid canister, and Block S120 definesa first horizontal endpoint of the selected area in alignment with acommon horizontal endpoint of the volume indicator markings. Block S120further defines a second horizontal endpoint of the selected area as amedian horizontal coordinate of pixels associated with the horizontalvolume indicator markings, a first vertical endpoint of the selectedarea as the bottom boundary of the fluid-containing portion of the fluidcanister, and a second vertical endpoint of the selected area on theidentified surface of the fluid in the fluid canister. From these fourendpoints, Block S120 can select and anchor a rectilinear area of theimage. This selected area can thus capture image color information alongthe full vertical height of fluid in the fluid canister ad substantiallyhorizontally centered within the isolated image of the fluid-containingportion of the fluid canister.

In a further example implementation, Block S120 can define the selectedarea that overlaps substantially completely with the reference markeridentified in Block S110. For example, Block S110 can identify thereference marker that is an anti-glare surface (e.g., anti-glare tape)on the canister, and Block S120 can define the selected area thatoverlaps substantially completely with the reference marker.

Block S120 can similarly recite correlating a segment of the image witha portion of the canister containing fluid, as shown in FIG. 5D. Forexample, Block S120 can identify a perimeter of the canister within theimage and cooperate with Block 130 to identify a surface of the fluidwithin the canister. Block S130 can then select a segment (i.e. area ofthe image) of the image bounded by the perimeter of the canister and thesurface of the fluid, the segment thus correlated with a portion of thecanister that contains fluid. Alternatively, Block S120 can characterizethe color of various pixels within a portion of the image correlatedwith the canister and select the segment that contains pixelscharacterized as substantially red (e.g., containing blood). However,Block S120 can function in any other way to select an area of the imagebased on the reference marker and/or correlate a segment of the imagewith a portion of the canister container fluid.

Block S130 of the method S100 recites correlating a portion of theselected area with a fluid level within the canister. Generally, BlockS130 functions to identify a surface of fluid in the canister and a baseof the canister (e.g., a lowest extent of fluid in the canister) and,from this data, estimate a level of fluid within the canister, as shownin FIG. 6A. As described above, the selected area can bisect the surfaceof the fluid, and Block S130 can therefore analyze the selected area toidentify the surface of the fluid. In one example implementation, BlockS120 can fit a parametric function (e.g. sigmoid) to an intensityprofile of the selected area that corresponds to an anti-glare strip onthe canister estimate a fluid height therein. In another exampleimplementation, Block S130 can correlate pixels in the selected areawith fluid (e.g. pixels that are substantially red) and calculate anupper bound and a lower bound of fluid in the canister based on thedistribution of y-coordinates of the correlated pixels. In this example,Block S130 can define the upper bound as the 95^(th) percentile of they-coordinates, or Block S130 can begin with the 99^(th) percentile ofthe y-coordinates of the correlated pixels and decrement the percentileuntil the redness of two adjacent pixels does not change beyond apredetermined threshold, though Block S130 can function in any other wayto identify and/or ignore “false-positive” ‘red’ pixels that do notcorrespond to fluid in the canister.

In one example implementation, Block S130 characterizes the color ofeach pixel (e.g., a redness value of each pixel) along a vertical lineof pixels within the selected area. By scanning the line of pixels fromthe bottom of the line of pixels (i.e. from proximal the base of thecanister) upward, Block S130 can identify a first abrupt shift in pixelcolor, which can be correlated with a lower bound of the surface of thefluid. By further scanning the line of pixels from the top of the lineof pixels (i.e. from proximal the top of the canister) downward, BlockS130 can identify a second abrupt shift in pixel color, which can becorrelated with an upper bound of the surface of the fluid. Block S130can average the upper and lower bounds of the surface of the fluid toestimate the level of the fluid within the canister. Alternatively,Block S130 can focus additional analysis on an abbreviated line ofpixels between the upper and lower bounds, such as by scanning up and/ordown the abbreviated line of pixels to identify more subtle changes inthe color of pixels along the line. For example, Block S130 cancorrelate a subtle lightening of pixel color of higher pixels with afluid meniscus. In another example, Block S130 can improve theresolution of the estimated surface of the fluid by reanalyzing pixelswithin subsequent abbreviated pixel lines.

Block S130 can similarly analyze two or more adjacent lines of pixelswithin the selected area and compare (e.g., average) results of eachpixel line analysis to improve accuracy of an estimate of the locationof the fluid surface. For example, Block S130 can compare the locationof one border pixel in each of a set of pixel lines in the selected areato extract a curved border between a fluid-filled portion of thecanister and an empty portion of the canister, and Block S130 cancorrelate this curved border with a fluid meniscus. Alternatively, BlockS130 can estimate the fluid meniscus. For example, Block S130 canimplement a lookup table of meniscus sizes and geometries, wherein thelookup table accounts for a type of canister, a fluid characteristic(e.g., redness value correlated with blood and water content in thecanister), an angle between the camera and the canister, a distancebetween the camera and the canister, the level of fluid in the canisterthat is conical, and/or any other suitable variable.

Block S130 can additionally or alternatively analyze clusters of pixels,such as four-pixel by four-pixel clusters in a four-pixel-wide line ofpixels within the pixel area. Block S130 can analyze discrete cluster orpixels or overlapping clusters or pixels, and Block S130 can average acharacteristic, such as a redness value or color property, of the pixelsin each cluster, such as to reduce error. However, Block S130 canfunction in any other way to identify the surface of the fluid in thecanister.

Block S130 can determine a lower bound of the fluid in the canister byimplementing similar methods of comparing pixel characteristics.Alternatively, Block can estimate the lower bound of the fluid to be ator proximal the determined lower boundary of the canister. However,Block S130 can function in any other way to identify the lower bound ofthe fluid in the canister.

Once Block S130 identifies both the upper and lower bounds of the fluidwithin the canister, Block S130 can calculate a pixel-based height ofthe fluid within the fluid canister, such as by counting the number ofpixels between the lower and upper bounds at approximately the center ofthe portion of the image correlated with the canister. Block S130 cansubsequently convert the pixel-based distance measurement to a physicaldistance measurement (e.g., inches, millimeters), such as by translatingthe pixel value according to the type of canister and/or an actual orestimated angle between the camera and the canister, distance betweenthe camera and the canister, geometry of the canister (e.g., diametersat the canister base and at the fluid surface), and/or any otherrelevant metric of or between the canister and the camera. Alternately,Block S140 can directly convert the pixel-based fluid level measurementinto an estimate fluid volume within the canister.

Block S120 can additionally or alternatively receive a manual input thatselects or identifies the reference marker, and Block S130 can similarlyadditionally or alternatively receive a manual input that selects oridentifies the surface of fluid or the height of fluid in the canister.For example, the method S100 can implement manual checks to teach orcorrect automatic selection of the reference marker and/or estimation ofthe canister fluid level. Block S120 and Block S130 can thus implementsupervised or semi-supervised machine learning to improve selection ofthe reference marker and/or estimation of the canister fluid level withsubsequent samples (i.e. images of one or more canisters). However,Block S120 and Block S130 can function in any other way to select thereference marker and/or estimate the canister fluid level, respectively.

Block S140 of the method S100 recites estimating a volume of fluidwithin the canister based on the fluid level. Generally, Block S140functions to convert the fluid level estimate of Block S130 into a fluidvolume estimate based on a canister type and/or geometry, as shown inFIGS. 1 and 7A. For example, the canister can be one of various types offrustoconical fluid canisters of various geometries (e.g., basediameter, sidewall angle, maximum fluid level) used in operating roomsand/or clinical settings to collect bodily fluids of patients.Therefore, once the type and/or geometry of the canister is entered by auser, determined through machine vision techniques, and/or accessed froma canister type and/or geometry database, Block S140 can transform thefluid level estimate into a fluid volume estimate. Furthermore, inimplementations in which Block S140 converts a pixel-based fluid levelmeasurement into a real fluid volume measurement, Block S140 can furtheraccount for an actual or estimated angle between the camera and thecanister, actual or estimated distance between the camera and thecanister, actual or estimated geometry of the canister (e.g., diametersat the canister base and at the fluid surface), and/or any otherrelevant metric of or between the canister and the camera.

In one example implementation, Block S110 implements object recognitionto determine the particular type of canister in the image, Block S130identifies a maximum number of pixels between the estimated surface ofthe fluid and the estimated bottom of the fluid canister, and Block S140accesses a lookup table for the particular type of canister. The lookuptable can correlate a maximum pixel number between the canister bottomand the fluid surface with canister fluid volume such that Block S140can enter the maximum pixel number calculated in Block S130 and returnthe fluid volume in the canister.

In another example implementation, Block S120 implements machine visiontechniques (e.g., edge detection) to determine the shape and/or geometryof the fluid canister, and Block S130 identifies the maximum number ofpixels between the surface of the fluid and the bottom of the fluidcanister and converts the pixel number to a physical dimension (e.g.,inches, millimeters) of fluid level in the canister. Block S140subsequently transforms the estimated fluid level into an estimatedtotal fluid volume in the canister according to an estimated physicalcross-section of the canister that is based on the determined the shapeand/or geometry of the fluid canister from Block S130.

In yet another example implementation, Block S110 implements machinevision techniques to identify fluid level markings printed (or embossed,adhered, etc.) on the fluid canister and Block S130 identifies thesurface of the fluid within the canister. Block S140 subsequentlyextrapolates the volume of fluid within the canister based on the fluidsurface and one or more fluid level markings adjacent the fluid surface.

Alternatively, Block S140 can access a direct fluid level measurementfrom a fluid level sensor coupled to (e.g., arranged in) the fluidcanister. Block S140 can also receive manual entry of a manual readingof the fluid level in the canister. For example, the method S100 canimplement manual checks to teach or correct automatic fluid volumeestimate. Block S140 can thus implement supervised or semi-supervisedmachine learning to improve canister fluid volume estimation over time.However, Block S140 can function in any other way to estimate orotherwise access a measurement of the volume of fluid in the canister.

Block S150 of the method S100 recites extracting a feature from theselected area. Generally, Block S150 functions to identify, in theselect are of the image of the canister, features indicative of aquality of fluid in the canister. For Block S160 that implementsparametric techniques to correlate the extracted featured with aconcentration of a blood component within the canister, Block S150 canextract the feature, from one or more pixels within the selected area,that is a color (red), a color intensity (e.g., redness value), aluminosity, a hue, a saturation value, brightness value, a gloss value,or other color-related value in one or more component spaces, such asthe red, blue, green, cyan, magenta, yellow, key, and/or Lab componentspaces. Block S150 can additionally or alternatively extract one or morefeatures that is a histogram of various color or color-related values ina set of pixels within the selected area. As shown in FIG. 6B, for BlockS160 that implements parametric techniques to correlate the extractedfeatured with a concentration of a blood component within the canister,Block S150 can extract the feature that is a cluster of pixels withinthe selected are and correlated with a portion of the canister thancontains fluid, such as a cluster of pixels that can be compared totemplate images in a library of template images of known blood componentconcentrations. However, Block S150 can extract any other suitablefeature from one or more pixels within the selected area.

Therefore, as shown in FIGS. 6A and 6B, Block S150 can extract featuresfrom multiple pixels within the selected area to collect a set offeatures indicative of fluid quality over the (full) height, width,and/or area of the select area correlated with a portion of the fluidcanister that contains fluid. For example, Block S150 can segment theselected area into m-pixel by n-pixel clusters of pixels, wherein an oby p array of pixel clusters substantially fills the selected area.Block S150 can then analyze each pixel cluster to extract one featureper pixel cluster. Block S150 can further average or otherwise combinefeatures from the pixel clusters to extract a single feature indicativeof fluid quality from the selected area. In another example, Block S150can segment the selected area into non-overlappingsingle-pixel-thickness (horizontal) rows extending across the full widthof the selected area. In this example, Block S150 can average pixelproperties in each row to extract a single feature from each row ofpixels. Similarly, Block S150 can segment the selected area intothree-pixel-thickness row sets extending across the full width of theselected area, wherein the outer single rows of each row set (except thelowermost and uppermost row sets) are shared with adjacent row sets, andwherein the pixels in each row set are averaged to extract a singlefeature from a set of pixels. Block S150 can additionally oralternatively segment the selected area into non-overlapping triangularpixel clusters, overlapping cross-shaped five-pixel arrays (shown inFIGS. 6A and 6B), overlapping circular pixel clusters and, from thesepixel clusters, extract one or more of the same or different types offeatures from the set of pixels. Block S150 can alternatively extract afeature from each individual pixel in the selected area or extract anyother number of features in any other way from information stored inpixels of the image bounded by the selected area.

Block S150 can additionally or alternatively extract one or morefeatures from the selected area, as described in U.S. patent applicationSer. No. 13/544,646, which is incorporated herein in its entirety by thereference. However, Block S150 can function in any other way to extracta feature from the selected area.

As described in U.S. patent application Ser. 13/544,646, Block S150 canfurther access non-image features, such as actual or estimated currentpatient intravascular hematocrit, estimated patient intravascularhematocrit, historic patient intravascular hematocrit, weight of thefluid canister or direct measurement of canister fluid volume,clinician-estimated canister fluid volume, fluid volumes and/orqualities of previous fluid canisters, previous fluid volumes and/orqualities of the fluid canister, an ambient lighting condition, a typeor other identifier of the fluid canister, directly-measured propertiesof fluid in the fluid canister, a patient vital sign, patient medicalhistory, an identity of a surgeon, a type of surgery or operation inprocess, or any other suitable non-image feature. For example, asdescribed below and in U.S. patent application Ser. No. 13/544,646,Block S160 and/or other Blocks of the method S100 can subsequentlyimplement any of these non-image features to select template images forcomparison with pixel clusters in the selected area, to select of aparametric model or function to transform the extracted feature(s) intoa blood component estimate, to define alarm triggers for excess fluid orblood loss, to transform one or more extracted features into a bloodquantity indicator, or to transform one or more extracted features intoa quantity or quality of an other fluid or solid in the fluid canister.However, the method S100 can implement any of these non-image featuresto modify, enable, or inform any other function of the method S100.

As shown in FIG. 2 Block S160 of the method S100 recites correlating theextracted featured with a concentration of a blood component within thecanister. As shown in FIG. 3, Block S160 can similarly recitecorrelating the color feature with a concentration of a blood componentwithin the canister. Generally, Block S160 functions to transform one ormore features (e.g., color feature) extracted from the image in BlockS150 into an estimated concentration of a blood component within fluidin the canister. As described above, the blood component can be any ofwhole blood, red blood cells, hemoglobin, platelets, plasma, white bloodcells, or any other blood component. For example, Block S160 canimplement parametric analysis techniques and/or non-parametric analysistechniques, such as described in U.S. patent application Ser. No.13/544,646, to estimate the concentration of the blood component withinthe fluid in the canister.

In one implementation, Block S150 extracts features from pixel clusterswithin the selected area of the image, and Block S160 tags each pixelcluster with a blood volume indicator based on a non-parametriccorrelation of each pixel cluster with a template image in a library oftemplate images of known blood component concentration. For example, asshown in FIG. 6A, Bock S150 can extract a color intensity in the redcomponent space from a set of pixel clusters, and Block S160 canimplement a K-nearest neighbor method to compare each extracted featurewith redness intensity values of template images. In this example, eachtemplate image can include a pixel cluster tagged with a known fluidquality, such as hemoglobin volume or mass per unit volume of fluid orper pixel unit (e.g., hemoglobin concentration). Each template image canadditionally or alternatively include a pixel cluster tagged with thevolume, mass, density, etc. per unit volume of fluid or per pixel unitof any other liquid or solid in the canister. Once Block S160 identifiesa suitable match between a particular pixel cluster and a particulartemplate image, Block S160 can project known fluid quality informationfrom the particular template image onto the particular pixel cluster.Block S160 can then aggregate, average, and/or otherwise combine pixelcluster tags to estimate output a total blood component concentrationfor the fluid in the canister. However, Block S160 can correlate theextracted featured with a concentration of a blood component within thecanister via any other suitable non-parametric method or technique.

In another implementation, Block S150 extracts features from pixelclusters within the selected area of the image, and Block S160implements a parametric model or function to tag each pixel cluster witha blood component concentration. As described in U.S. patent applicationSer. No. 13/544,646, Block S160 can insert one or more extractedfeatures from one pixel cluster into a parametric function tosubstantially directly transform the extracted feature(s) from the pixelcluster into a blood component concentration. Block S160 can then repeatthis for each other pixel cluster in the selected area. In one example,the extracted feature(s) can include any one or more of a colorintensity in the red component space, a color intensity in the bluecomponent space, an/or a color intensity in the green component space.In this example, the parametric function can be a mathematical operationor algorithm that relates color intensity to hemoglobin mass per unitfluid volume. As described in U.S. patent application Ser. No.13/544,646, reflectance of oxygenated hemoglobin (HbO₂) at certainwavelengths of light can be indicative of the concentration ofhemoglobin per unit volume of fluid. Therefore, in another example,Block S150 can extract a reflectance values at a particular wavelengthfor each of a set of pixel clusters in the selected area, and Block S160can convert each reflectance value into a hemoglobin concentration valueby implementing a parametric model. Block S160 can then combine thehemoglobin concentration values to estimate the total (i.e. average)hemoglobin concentration in the canister. Furthermore, because thehemoglobin content of a wet (hydrated) red blood cell is typically about35%, red blood cell concentration can be extrapolated from thehemoglobin concentration based on a static estimated hemoglobin content(e.g., 35%). Furthermore, Block S150 can access a recent measuredhematocrit or estimate a current hematocrit of the patient (as describedin U.S. Provisional Application No. 61/646,822), and Block S160 canimplement the measured or estimated hematocrit to transform theestimated red blood cell concentration into an estimates extracorporealblood concentration. However, Block S160 can implement any otherparametric and/or non-parametric analysis of single pixels or pixelclusters within the selected area to estimate the concentration of anyone or more blood components in fluid within the canister.

Block S170 of the method S100 recites estimating a quantity of the bloodcomponent within the canister based on the estimated volume and theconcentration of the blood component within the canister. Generally,Block S170 functions to calculate a quantity (e.g., mass, weight,volume, cell count, etc.) of the blood component by multiplying theestimated volume of fluid in the canister by the estimated concentrationof the blood component in the fluid in the canister, as shown in FIGS.7A and 7B. For example Block S170 can estimate a red blood cell countwithin the canister or a total extracorporeal blood volume in the fluidcanister. Block S170 can further interface with a method of U.S. patentapplication Ser. No. 13/544,646 to combine an estimate blood volume inthe canister with estimated blood volumes in surgical gauze sponges,surgical towels, surgical drapes, and/or surgical dressings to estimatetotal patient blood loss, such as in Block S190 described below.However, Block S170 can function in any other way to estimate a quantityof the blood component within the canister

As shown in FIG. 3, one variation of the method S100 includes BlockS180, which recites extracting a second feature from the selected area,correlating the second extracted featured with a concentration of anon-blood component within the canister, and estimating a quantity ofthe non-blood component within the canister based on the estimatedvolume and the concentration of the non-blood component within thecanister. Generally, Block S180 implements methods similar to those ofBlock S150, S160, and/or S170 to estimate a content (e.g., quantity) ofa non-blood component within the canister. As described above, thenon-blood component can be saline, ascites, bile, irrigant saliva,gastric fluid, mucus, pleural fluid, urine, fecal matter, or any otherbodily fluid of a patient, surgical fluid, particulate, or matter in thecanister.

In one implementation similar to Blocks S150, S160, and S170 thatestimate the blood component content in the fluid canister based oncolor properties (e.g., ‘redness’) of the fluid in the fluid canister,Block S180 analyzes other color properties of the fluid to estimate thecontent of other matter in the canister. For example, Block S180 cananalyze the clarity of the fluid in the canister and correlate theestimated clarity of the fluid with a concentration or content of wateror saline in the fluid canister. In another example, Block S180 canextract a ‘yellowness’ (e.g., color intensity in the yellow componentspace) of the fluid and correlate the yellowness with a concentration orcontent of plasma and/or urine in the fluid canister. Similarly, BlockS150 can extract a ‘greenness’ (e.g., color intensities in the green andyellow component spaces) of the fluid and correlate the greenness with aconcentration or content of bile in the fluid canister. However, BlockS180 can estimate the quantity and/or concentration of any other fluid,particulate, or matter in the fluid canister.

As shown in FIG. 3, one variation of the method S100 includes BlockS190, which recites estimating total patient blood loss based on theestimated volume of blood in the canister. For example, Block S190 cansum the estimate blood volume with an estimated blood volume of one ormore previous canisters, such as shown in FIG. 8. Furthermore, asdescribed above, Block S190 can combine the canister blood volumeestimate of Block S170 with interface with estimated blood volumes insurgical gauze sponges, surgical towels, surgical drapes, and/orsurgical dressings as described in U.S. patent application Ser. No.13/544,646. Block S190 can also add fluid canister blood volume data toa medical record of a patient, trigger an alarm once a thresholdextracorporeal blood volume estimate is reached, or estimate currentpatient hematocrit based on initial patient hematocrit, fluid IVs,transfusions, and total estimated blood loss, such as described in U.S.Provisional Application No. 61/646,822. Block S190 can also estimate afuture time at which the patient's intracirculatory blood volume,intracirculatory hematocrit, intracirculatory blood viscosity, and/orintracirculatory red blood cell content, etc. will fall outside of anacceptable window. For example, Block S190 can determine that currenttotal patient blood loss and patient intracirculatory hematocrit arewithin acceptable bounds but that an increasing blood loss rate willresult in excessive blood loss at a particular time in the future (e.g.,in approximately five minutes). Block S160 can accordingly determine afuture patient need for autologous or allogeneic blood transfusion, asaline drip, etc. based on trends in patient blood-related parameters.Block S190 can therefore estimate patient risk based on estimated bloodloss, trigger administration of a blood transfusion once a blood lossthreshold is reached, and/or estimate future patient needs or risk basedon trends in estimated blood loss. However, Block S190 can function inany other way to maintain a substantially comprehensive estimate oftotal patient blood (and fluid) loss, such as during a surgery or othermedical event.

As shown in FIG. 3, one variation of the method S100 includes BlockS192, which recites displaying results of analysis of the canister, suchas the total fluid volume, estimated hemoglobin content, red blood cellcontent, extracorporeal blood volume, etc. in the fluid canister. Asshown in FIGS. 1, 7A, and 7B, Block S192 can control an augmentedreality overlay on top of a static image or live video feed of the fluidcanister also renders on the display. For example, in an implementationin which Blocks of the method S100 are implemented by a mobile computingdevice (e.g., smartphone, tablet), a display integral with the computingdevice can display the estimated current blood volume and/or hematocritquantity in the canister. The display can also render estimated bloodvolumes and/or hematocrit quantities in the fluid canister over a periodof time, a total estimated current blood volume and/or hematocritquantity in the fluid canister and scanned surgical sponges, and/orpast, current, and predicted future total estimated blood volume and/orhematocrit quantities in the fluid canister and scanned surgicalsponges. Block S192 can additionally inform a user of a single pastfluid quality and/or volume, multiple past fluid qualities and/orvolumes, and/or a trend in fluid quality and/or volume over time.However, Block S192 can function to display any relevant fluid canister(and sponge content information to a user in any other way.

2. Systems

As shown in FIG. 9, a system 100 for estimating a quantity of a bloodcomponent in a fluid canister includes: an optical sensor 110; aprocessor 120 coupled to the optical sensor 110 to capture an image of acanister, the software module 122 further instructing the processor 120to select an area of the image correlated with a portion of the canistercontaining fluid, to estimate a volume of fluid within the canisterbased on the selected area, to extract a feature from the selected area,and to estimate a quantity of a blood component within the canisterbased on the extracted feature; and a display 130 coupled to theprocessor 120 and receiving instruction from the software module 122 todisplay the quantity of the blood component within the canister.

The system 100 functions to implement the method S100 described above,wherein the optical sensor (e.g., camera) implements Block S102 tocapture the image of the canister, the processor implements Blocks S110,S120, S130, S140, S150, S160, S170, etc. described above to estimate thequantity and quality of fluid in a surgical suction canister. The system100, optical sensor, processor, and display can include and/or functionas any one or more components described in U.S. patent application Ser.No. 13/544,646. A surgeon, nurse, anesthesiologist, gynecologist,doctor, soldier, or other user can use the system 100 to estimate thequantity and/or quality of a fluid collected in fluid canister, such asduring a surgery, child birth, or other medical event. The system 100can also detect presence of blood in the canister, compute patient bloodloss rate, estimate patient risk level (e.g., hypovolemic shock), and/ordetermine hemorrhage classification of a patient. However, the system100 can perform any other suitable function.

The system 100 can be configured as a handheld (e.g., mobile) electronicdevice, such as a smartphone or tablet running an image-based bloodestimation application (or app) and including the optical sensor 110,the processor 120, and the display 130. Alternatively, the components ofthe system 100 can be substantially discreet and distinct (i.e., notcontained within a single housing). For example, the optical sensor 110can be a camera substantially permanently arranged within an operatingroom, wherein the camera communicates with a local network or a remoteserver (including the processor 120) on which the image of the canisteris analyzed (e.g., according to the method S100), and wherein a display130 that is a computer monitor, a television, or a handheld (mobile)electronic device accesses and displays the output of the processor 120.However, the system 100 can be of any other form or include any othercomponent.

The system 100 can be used in a variety of settings, including in ahospital setting, such as in a surgical operating room, in a clinicalsetting, such as in a delivery room, in a military setting, such as on abattlefield, or in a residential setting, such as aiding a consumer inmonitoring blood loss due to menorrhagia (heavy menstrual bleeding) orepistaxis (nosebleeds). However, the system 100 can be used in any othersetting.

The optical sensor 110 of the system 100 functions to capture the imageof the canister. The optical sensor 110 functions to implement BlockS102 of the method S100 and can be controlled by the software module122. In one example implementation, the optical sensor 110 is a digitalcamera that captures a color image of the canister or an RGB camera thatcaptures independent image components in the red, green, and bluefields. However, the optical sensor 110 can be any number and/or type ofcameras, charge-coupled device (CCD) sensors, complimentarymetal-oxide-semiconductor (CMOS) active pixel sensors, or opticalsensors of any other type. However, the optical sensor 110 can functionin any other way to capture the image of the canister, such as in anysuitable form or across any suitable visible or invisible spectrum.

In one implementation, the optical sensor 110 is a camera arrangedwithin a handheld electronic device. In another implementation, theoptical sensor 110 is a camera or other sensor configured to be mountedon a pedestal for placement in an operating room, configured to bemounted to a ceiling over an operating table, configured for attachmentto a battlefield helmet of a field nurse, configured to mount to astandalone blood volume estimation system including the processor 120,the display 130, and a staging tray that supports the canister forimaging, or configured for placement in or attachment to any otherobject or structure.

The software module 122 can also control the optical sensor 110, such asby auto-focusing or auto-exposing the optical sensor. Additionally oralternatively, the software module 122 can filter out poor-qualityimages of the canister and selectively pass high- or sufficient-qualityimages to the processor 120 for analysis.

According to instructions from the software module 122, the processor120 of the system 100 receives the image of the canister, estimates avolume of fluid within the canister, extracts a feature from an area ofthe image correlated with the volume of fluid, correlates the extractedfeatured with a concentration of a blood component within the canister,and estimates a quantity of the blood component within the canisterbased on the estimated volume and the concentration of the bloodcomponent within the canister. The processor 120 can therefore implementBlocks of the method S100 described above according to instructions fromthe software module 122. The processor 120 can also analyze differenttypes of images (e.g., static, streaming, .MPEG, .JPG, .TIFF) and/orimages from one or more distinct cameras or optical sensors.

The processor 120 can be coupled to the optical sensor 110, such as viaa wired connection (e.g., a trace on a shared PCB) or a wirelessconnection (e.g., a Wi-Fi or Bluetooth connection), such that theprocessor 120 can access the image of the canister captured by theoptical sensor 1100 r visible in the field of view of the optical sensor110. In one variation, the processor 120 is arranged within a handheldelectronic device that also contains the optical sensor 110 and thedisplay 130. In another variation, the processor 120 is a portion of oris tied to a remote server, wherein image data from the optical sensor110 is transmitted (e.g., via an Internet or local network connection)to the remote processor 120, wherein the processor 120 estimates theextracorporeal blood volume in at least the portion of the canister byanalyzing the image of the canister, and wherein the blood componentvolume estimate is transmitted to the display 130.

In one implementation and as described above, the processor 120 can pairthe portion of the image of the canister to a template image viatemplate matching, and the template image is one template image in alibrary of template images. For example, the system can further includea data storage module 160 configured to store a library of templateimages of known concentrations of the blood component. In thisimplementation, the processor can correlate the extracted featured withthe concentration of the blood component by comparing the extractedfeature with a template image in the library of template images, asdescribed above. Alternatively and as described above, the processor 120implements a parametric model to estimate the quantity of the bloodcomponent in the canister based on a feature extracted from the image.

The software module 122 of the system 100 functions to control theoptical sensor 110, the processor 120, and the display 130 to capturethe image of the camera, analyze the image, and display results of theanalysis. The software module 122 can execute on the processor as anapplet, a native application, firmware, software, or any other suitableform of code to control processes of the system 100. Generally, thesoftware module controls application of Blocks of the method S100described above, though the software module 122 can control and/orimplement any other suitable process or method on or within the system100.

In one example application, the software module 122 is a nativeapplication installed on the system 100 that is a handheld (i.e. mobile)computing device, such as a smartphone or tablet. When selected from amenu within on operating system executing on the computing device, thesoftware module 122 opens, interfaces with a user initialize a new case,controls the optical sensor 110 integrated into the computing device tocapture the image, implements machine vision and executes mathematicalalgorithms on the processor to estimate the quantity of the bloodcomponent, and controls the display 130 to render the estimated quantityof the blood component. However, the software module 122 can be of anyother form or type and can be implemented in any other way.

The display 130 of the system 100 depicts the estimated quantity of theblood component in the canister. The display 130 can be arranged withinthe handheld electronic device (e.g., smartphone, tablet, personal dataassistant) that also contains the optical sensor 110 and the processor120. Alternatively, the display can be a computer monitor, a televisionscreen, or any other suitable display physically coextensive with anyother device. The display 130 can be any of an LED, OLED, plasma, dotmatrix, segment, e-ink, or retina display, a series of idiot lightscorresponding to estimated quantity of the blood component, or any othersuitable type of display. Finally, the display 130 can be incommunication with the processor 120 via any of a wired and a wirelessconnection.

The display 130 can perform at least Block S192 of the method S100 bydepicting the estimated quantity of the blood component in the canisterand/or in multiple canisters. The blood volume estimate can be depictedin a common form, such as “ccs” (cubic centimeters). As described above,this data can be presented in the form of a dynamic augmented realityoverlay on top of a live video stream of the canister that is alsodepicted on the display 130, wherein images from the optical sensor 110are relayed substantially in real time, through the processor 120, tothe display 130. The data can alternatively be presented in a table,chart, or graph depicting at least one of a time-elapse cumulativeestimated quantity of the blood component across multiple samplesanalyzed over time and individual blood volume estimates for eachcanister. the display 130 can also render any of a previous image of thecanisters, warnings, such as patient risk level (e.g., hypovolemicshock), or a hemorrhage classification of the patient, or suggestions,such as to begin blood transfusion. Any off these data, warnings, and/orsuggestions can also be depicted across multiple screens or madeavailable for access on any one of more displays.

One variation of the system 100 further includes a handheld housing 140configured to contain the optical sensor 110, processor 120, and display130. The handheld housing 140, with optical sensor 110, processor 120,and display 130, can define a handheld (mobile) electronic devicecapable of estimating blood volume in one or more canisters in anynumber of suitable environments, such as in an operating room or adelivery room. The housing 140 can be of a medical-grade material suchthat the system 100 that is a handheld electronic device can be suitablefor use in an operating room or other medical or clinical setting. Forexample, the housing can be medical-grade stainless steel, such as 316Lstainless steel, a medical-grade polymer, such as high-densitypolyethylene (HDPE), or a medical-grade silicone rubber. However, thehousing can be of any other material or combination of materials.

In one variation of the system 100, the system 100 further includes awireless communication module 150 that communicates the estimatedquantity of the blood component in the canister to a remote serverconfigured to store an electronic medical record of a patient. Thesystem can also update the medical record with estimated blood loss overtime, patient risk level, hemorrhage classification, and/or otherblood-related metrics. The patient medical record can therefore beupdated substantially automatically during a medical event, such as asurgery or childbirth.

The systems and methods of the preferred embodiments can be embodiedand/or implemented at least in part as a machine configured t receive acomputer-readable medium storing computer-readable instructions. Theinstructions are executed by computer-executable components integratedwith the system, the optical sensor, the processor, the display,hardware/firmware/software elements of a system or handheld computingdevice, or any suitable combination thereof. Other systems and methodsof the preferred embodiments can be embodied and/or implemented at leastin part as a machine configured to receive a computer-readable mediumstoring computer-readable instructions. The instructions are executed bycomputer-executable components integrated by computer-executablecomponents integrated with apparatuses and networks of the typedescribed above. The computer-readable medium can be stored on anysuitable computer readable media such as RAMs, ROMs, flash memory,EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or anysuitable device. The computer-executable component is a processor butany suitable dedicated hardware device can (alternatively oradditionally) execute the instructions.

As a person skilled in the art of estimating the extracorporeal bloodvolume in a canister will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A computer-implemented method for assessing fluid in afluid container, comprising: receiving an image of at least a portion ofthe fluid container, wherein a fluid is in the fluid container;extracting at least one color-related feature from an area of the imagedepicting at least a portion of the fluid in the fluid container;correlating the at least one extracted color-related feature with aconcentration of a blood component in the fluid in the fluid container;estimating a quantity of the blood component in the fluid containerbased on the concentration of the blood component and an estimatedvolume of blood in the fluid container; and communicating the estimatedquantity of the blood component to a user.
 2. The method of claim 1,further comprising generating an estimated volume of blood in the fluidcontainer with a volume estimation algorithm.
 3. The method of claim 2,further comprising selecting the area of the image depicting at least aportion of the fluid in the fluid container, wherein the extractedfeature is extracted from the selected area of the image.
 4. The methodof claim 1, further comprising receiving a manual reading of a fluidlevel in the fluid container.
 5. The method of claim 1, wherein the atleast one extracted color-related feature comprises at least one ofcolor intensity, luminosity, hue, saturation value, brightness value,and gloss value.
 6. The method of claim 1, wherein extracting the atleast one color-related feature comprises extracting a set of featuresfrom a plurality of pixel clusters in the image, wherein the set offeatures includes one feature per pixel cluster.
 7. The method of claim6, wherein correlating the at least one extracted color-related featurecomprises correlating each extracted feature in the set of features witha respective concentration of a blood component in the fluid container.8. The method of claim 7, wherein estimating a quantity of the bloodcomponent in the fluid container comprises estimating the quantity ofthe blood component in the fluid container based on the concentrationsof the blood component and the estimated volume of blood in the fluidcontainer.
 9. The method of claim 7, wherein correlating the at leastone extracted color-related feature comprises comparing the at least oneextracted color-related feature to a reference value of a template imageof known concentration of the blood component.
 10. The method of claim7, wherein correlating the at least one extracted color-related featurecomprises transforming the at least one extracted color-related featureinto the concentration of the blood component according to a parametricmodel.
 11. The method of claim 1, wherein communicating the estimatedquantity of the blood component comprises displaying the estimatedquantity of the blood component on a display to the user.
 12. A systemfor assessing fluid in a fluid container, the system comprising: anoptical sensor configured to capture an image of at least a portion ofthe fluid container, wherein a fluid is in the fluid container; one ormore processors coupled to the optical sensor and configured to receivethe image, extract at least one color-related feature from an area ofthe image depicting at least a portion of the fluid in the fluidcontainer, correlate the at least one extracted color-related featurewith a concentration of a blood component in the fluid container, andestimate a quantity of the blood component in the fluid container basedon the concentration of the blood component and an estimated volume ofblood in the fluid container; and a display configured to communicatethe estimated quantity of the blood component to a user.
 13. The systemof claim 12, wherein the one or more processors is configured to selectan area of the image depicting at least a portion of the fluid in thefluid container, wherein the extracted feature is extracted from theselected area of the image.
 14. The system of claim 12, wherein the oneor more processors is configured to receive a manual reading of a fluidlevel in the fluid container.
 15. The system of claim 12, wherein the atleast one extracted color-related feature comprises at least one ofcolor intensity, luminosity, hue, saturation value, brightness value,and gloss value.
 16. The system of claim 12, wherein the one or moreprocessors is configured to extract a set of features from a pluralityof pixel clusters in the image, wherein the set of features includes onefeature per pixel cluster.
 17. The system of claim 16, wherein the oneor more processors is configured to correlate each extracted feature inthe set of features with a respective concentration of a blood componentin the fluid container.
 18. The system of claim 17, wherein the one ormore processors is configured to estimate the quantity of the bloodcomponent in the fluid container based on the concentrations of theblood component and the estimated volume of blood in the fluidcontainer.
 19. The system of claim 16, wherein the one or moreprocessors is configured to correlate the at least one extractedcolor-related feature by comparing the at least one extractedcolor-related feature to a reference value of a template image of knownconcentration of the blood component.
 20. The system of claim 16,wherein the one or more processors is configured to correlate the atleast one extracted color-related feature by transforming the at leastone extracted color-related feature into the concentration of the bloodcomponent according to a parametric model.